Meta Llama
Open-weight AI models with frontier capability. We deploy Llama for applications requiring data privacy, custom deployment or full model control.
The Llama model family
Meta's Llama models are open-weight, meaning you can inspect, customise and deploy them on your own infrastructure. Multiple sizes serve different requirements.
Llama Large
The most capable Llama model. Competitive with proprietary alternatives on reasoning, coding and complex instruction following.
Llama Medium
Strong performance with lower compute requirements. A practical choice for production workloads balancing quality and cost.
Llama Small
Lightweight models suitable for edge deployment, on-device inference or high-volume tasks where latency matters most.
What makes Llama different
Open weights change the economics and control dynamics of AI deployment fundamentally.
Open weights
Inspect model architecture and weights. Understand what you are deploying and modify it to suit your needs.
Data sovereignty
Run models on your own infrastructure. Data never leaves your environment, meeting strict privacy and compliance requirements.
Custom fine-tuning
Full fine-tuning, LoRA and other adaptation techniques are available to specialise models for your domain.
No vendor lock-in
Deploy on any cloud provider or on-premises. Switch infrastructure without changing your model or application code.
Cost predictability
Self-hosted deployment means compute costs, not per-token pricing. At scale, this can significantly reduce total cost.
Community ecosystem
Large open-source community providing tooling, adapters, quantised versions and deployment patterns.
Use cases for Llama
Regulated industries
Healthcare, finance and government applications where data must not leave controlled environments.
On-premises deployment
Organisations with existing GPU infrastructure that want to run AI without cloud dependencies.
Domain-specific models
Fine-tuning Llama on specialised data to create models that outperform general-purpose alternatives in specific domains.
Edge and mobile
Smaller Llama variants running on devices for offline capability and reduced latency.
High-volume processing
Batch processing workloads where self-hosted models provide better economics than per-token API pricing.
Research and experimentation
Teams that need to understand model behaviour deeply, run ablation studies or develop custom capabilities.
Deployment options
Llama can be deployed in multiple ways depending on your infrastructure preferences and operational requirements.
Cloud hosted
Run on AWS, Azure or GCP using managed ML services like SageMaker, Azure ML or Vertex AI.
On-premises
Deploy on your own GPU infrastructure for maximum control over data and compute resources.
Managed APIs
Access Llama through Amazon Bedrock, Azure or specialised inference providers without managing infrastructure.
Frequently Asked Questions
Is Llama really free to use?
Llama is free to download and use under Meta's licence for most commercial purposes. You pay for compute infrastructure to run it, not for the model itself.
How does Llama compare to proprietary models?
The largest Llama models are competitive with proprietary alternatives on many tasks. The trade-off is operational complexity in exchange for control and cost predictability.
Do we need our own GPUs?
Not necessarily. Llama is available through managed services like Amazon Bedrock. Self-hosting requires GPU infrastructure but gives maximum control.
Can we fine-tune Llama on our own data?
Yes. Full fine-tuning and parameter-efficient methods like LoRA are well supported, making it practical to specialise Llama for your domain.
Build with Llama
We help organisations deploy open-weight models effectively. Book a call to discuss whether Llama is right for your use case.